This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Objective: To generate early warning diagnostics for increased metabolic syndrome risk that will facilitate intervention and preventative treatment, and provide insights into the mechanistic basis of the disease. Metabolic syndrome alters serum factors that influence metabolism and inflammation, including lipoproteins, fatty acids and adipokines. We are using the rhesus macaque model to determine whether levels of these factors in the pre-diseased state can be predictive of future disease development. This study tests the hypothesis that lipoprotein, fatty acid, and adipokine pro-inflammatory profiles, in combination, constitute an early predictor of metabolic syndrome development. Specific Aim1: To determine the lipoprotein particle size distribution profile in serum from healthy controls and impaired animals at the time of diagnosis and 2 years before disease onset. Specific Aim 2: To quantify fatty acid concentration and composition in serum from healthy and impaired animals before and after disease onset. Specific Aim 3: To determine levels of adipokines and pro-inflammatory factors in serum from healthy and impaired animals before and after disease onset. Rationale: Metabolism and inflammation are linked through nuclear receptor family of transcription factors that act as mediators of both processes. These key regulatory molecules are in turn influenced by protein and lipid serum factors, many of which are adipose-tissue derived. Increased adiposity influences serum levels of adipokines and lipokines that may contribute to increased risk for metabolic syndrome. The goal of this study is to provide a critical early diagnostic that would dramatically improve outcomes for obese pediatric patients that are at risk of metabolic syndrome development. In addition, data generated in this study could provide novel leads for the development of therapeutic strategies. Identification of early-responding factors in the pre-diseased state will advance our understanding of the underlying biology of the metabolic dysfunction that is the basis for this increasingly prevalent disease. Animals: The cohort for this study was selected as a subset of male rhesus monkeys from the "Dietary restriction and Aging" Program Project at the WNPRC. As the analysis involves plasma only, we were able to rely on banked samples. The criteria for selection include a) metabolic syndrome animals with clinical manifestation of insulin resistance with serum available at time of diagnosis and 2 years prior to onset of disease b) impaired animals were matched with healthy animals of similar age and body weight and c) dietary and living conditions were controlled to limit influences that could interfere with the analysis. Insulin sensitivity and biometric data were provided by WNPRC. Results: In order to generate a predictive diagnostic we investigated factors that are responsive to changes in metabolism and also play a role in inflammation. Serum factors that are known to be influenced by increased visceral adiposity include lipoprotein size and distribution, fatty acid concentration and composition, and adipokine and adipose-derived proinflammatory cytokines. Lipoprotein profiles: HDH, LDL, VLDL particle size and distribution have been determined for n=8 metabolic syndrome animals at time of diagnosis and at 2 years prior to onset of disease along with age and weight matched healthy control animals. A preliminary analysis of the blind data clearly identifies animals with metabolic syndrome and points to potential candidates as early-responders. Serum fatty acid concentration and composition: The constituent fatty acids within 5 lipid groups have been determined for one third of the cohort at this time. Lipids were extracted from plasma and separated into i) cholesterol esters, ii) phospholipids, iii) diacylglycerol, iv) triglycerides and v) free fatty acids. Gas chromatographic analysis detects 28 distinct fatty acid species that are present in discreet proportions and populations among the lipid groups. Preliminary analysis can distinguish the impaired animals from the healthy controls, although it is too early in the data collection (50% complete at time of report) and analysis to identify candidates for a predictive diagnostic. Adiokine and cytokine profile: Data have been collected for CRP and we have validated the bio-assays for detection of adipokines. We anticipate completion of this data set within a matter of weeks. Data analysis: The complete dataset will be analyzed in separately and in combination to determine responsive factors that can be associated with impending onset of metabolic syndrome. These analyses will be conducted in collaboration with our colleagues at the Department of Biostatistics and Medical Informatics. This research uses WNPRC Animal Services and Research Services.